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STRHITS Vl-:RSliS SlM rilS: PUl^l.R' Pl-.RC'l'.P riONS AI^Ol 1 T Ti IP: SI'RIOUSNI'SS ()!■ 

wniri:-c()i.i,AR CR1MI-: 

Victoria Van Antwerp 
B.A in Sociology. University of Colorado I^ouldcr, cum laudc, 2009 
B.A. in Women Studies, I Iniversity of Colorado Boulder, 2009 



A thesis submitted to the 
University of Colorado Denver 
in partial fulfillment 
of the requirements for the degree of 
Master of Criminal Justice 
2011 



20! 1 by Victoria \*aii Antwerp 
All rights rcscrv cd. 



This thesis for the Master of Criminal Justice 
degree by 
Victoria Van Antwerp 
has been approved 
by 




Cr^He M. Rennison, Ph.D. 




Tracie Keesee, Ph.D. 



Date 



Van Antwerp, Victoria (M.C.J.) 

Suites versus Streets: Public Perceptions about the Seriousness of White-Collar Crime 
Thesis directed by Associate Professor Mary J. Dodge 

ABSTRACT 

The following research explores public perceptions of white-collar crime. Participants 
were asked to complete a two-page survey, inquiring about their perceptions of. Six 
white-collar crime scenarios and six street crime scenarios. Surveys were distributed 
to jury participants after they had been dismissed from jury. Participants were asked 
to read the crime scenarios and then judge the offense and offender on: seriousness, 
appropriate punishment for the offender, greed, remorse, and stress. Analyze revealed 
that public opinion on white-collar crimes has shifted. Overall, the public perceives 
white-collar crime to be just as serious as street crime, if not more serious. Change in 
public perception about white-collar crime may stem from the media focus on high 
profile incidents such as Enron, WorldCom, Martha Stewart's insider trading, and 
Bernard Madoff s Ponzi scheme. 

This abstract accurately represents the content of the candidate's thesis. I recommend 
its publication. 



DEDICATION 



I dedicate my thesis to my family, Mary Dodge and Wyatt Kennedy. Each of you 
holds a special place in my heart, and in this thesis process. 

My family has given me an appreciation of learning, and taught me the values of 
perseverance, dedication, and determination; without these values and your 
unconditional support, this thesis would not have been possible. 

Mary, I could never begin to thank you for everything you have done for me, without 
you none of this would have been possible. You truly inspire me! 

Wyatt, thank you for your unfaltering support and understanding while I completed 
this thesis, I could not have done it without your support, understanding, and 
encouragement. 



ACKNOWLEDGEMENT 



My deepest thanks to my advisor, Mary Dodge, your support, expertise, and 
contribution to my researcli made this thesis possible. You always went above and 
beyond, and I can never tell you how grateful I am for all your help. I also want to 
thank Callie Rennison and Tracie Keesee for all your valuable participation and 
insights throughout this process. Callie, 1 could not have produced the results I did 
without your guidance and instruction on appropriate testing procedures. I am truly 
appreciative of all the time you spend with me to create an amazing polished product. 
Tracie, you taught me how to look through several different lenses, without those 
lenses, this process would have been a view without multiple perspectives. I cannot 
begin to tell all of you how much I appreciate all the time, and dedication you showed 
to my research process and project. You three truly helped make this process easier, 
and helped create a great thesis. 

I am incredibly grateful for the judicial staff in the El Paso County Courthouse, who 
aided in gathering participants for the research study, approving the research study to 
take place within the El Paso County courthouse, and allowed me to sequester jurors 
within the courthouse during the research period. Particularly, the Jury 
Commissioner, Mr. Dennis McKinney, for continually helping gather research 
participants for me, and for helping get the study approved through the proper 
channels in El Paso County. Assistant Jury Commissioner's Leilani Hendrick and 
Michelle Flesher are also owed many thanks for their assistance, and support during 
the research process. Lastly, the principal investigator is indebted to the individuals of 
El Paso County who chose to participate in the research study. Without their help in 
this research process, this research would not have been possible. 

I also owe a thank you to my family. To my parents. Ken and Kathy Van Antwerp, 
thank you for believing in me, and encouraging me to finish this thesis out, even 
when I didn't believe in myself. Grandpa, you were always there to listen to me read 
out loud my articles, I can't tell you how much it helped to have an extra ear to listen 
to me babble on. Uncle Bob, thanks for your unconditional support, and for listening 
to my stories about data collecting. Yetti, thanks for giving up two weeks of your 
time to help me out with my research. 1 could never have gotten through the research 
as quickly as I did without all your help. You also were an awesome help doing data 
entry! Thanks so much everyone for everything. 

To my great friends: Carolyn Caputo, Derek Bauer, David Dean, Dana Reynolds, 
Donessa Caspar, Jack Thorpe, Rachel Freeman, Razan Naqeeb, and Tim Jones, thank 



you for reminding me that you need to have fun, and laugh even in the most stressful 
times. 

I would also like to thank the School of Public Affairs staff and faculty. Particularly 
the staff and faculty that helped me through parts of this process: Antoinette 
Sandoval, Brendan Hardy, Christopher Weible, Dawn Savage, Jen Gartner, Kaylene 
McCrum, Lisa Carlson, and Rob Drouillard. Each of you encouraged me, inquired 
how the thesis process was going, and helped me through some part of the process I 
am grateful for everything you did for me during this process, and for the help you 
give the students of the School of Public Affairs. Each of you helps make SPA the 
best place to be! 

Wyatt, you were truly a rock through this thesis. I cannot even begin to put in words 
how much your support meant to me. You were always willing to pick-up the slack 
and encourage me when I felt this was just impossible. Your unfaltering belief in me 
meant more to me than I can ever begin to tell you! Thank you for everything my 
dear! 



LIST OF TABLES 



Table 

4. 1 Descriptive Statistics for the dependent variables (Ponzi Scheme, 
Embezzlement, Auto Theft) 29 

4.2 Descriptive Statistics for the dependent variables (Burglary, Corporate Crime, 



and Prostitution) 30 

4.3 Descriptive statistics, percentage, means and standard deviations for sample, 
n=900 31 

4.4 Regression analyses for Ponzi crimes 34 

4.5 Regression analyses for Embezzlement crimes 37 

4.6 Regression analyses for Auto-Theft crimes 41 

4.7 Regression analyses for Burglary crimes 44 

4.8 Regression analyses for corporate crimes 47 

4.9 Regression analyses for prostitution crimes 50 



4. 1 Binary Logistic Regression analyses for White Collar and Street Crimes ... .52 



TABLE OF CONTENTS 



Tables 

viii 

Preface 

ix 

CHAPTER 

1. INTRODUCTION ^ 

2. LITERATURE REVIEW 

J 

Public Perception of White Collar Crime 4 

Gender and White-Collar Crime 9 

Public Perception on Sentencing 1 3 

3. RESEARCH HYPOTHESIS 21 

4. METHODOLOGY 

Participants 2^ 

Survey Instrument ^2 

Analytic Technique 27 

5. RESULTS 28 

Ordinary Least Square Analyses 31 

Binary Logistic Regression Analysis 51 

6. DISCUSSION AND CONCLUSIONS 53 

APPENDIX 

A. QUESTIONNAIRES 

B. HUMAN SUBJECTS APPROVAL 

REFERENCES 57 



vii 



PREFACE 

'A criminal is a person with predatory instincts who has not sufficient capital to form 
a corporation.' - Clarence Darrow 



ix 



CHAPTER 1 
INTRODUCTION 

In 1939, at the American Sociological Society convention, Edwin 

Sutherland's presidential address introduced and defined white-collar crime. 

According to Sutherland (1949), white-collar crime constituted "a crime committed 

by a person of respectability and high social status in the course of his occupation" (p. 

9). Currently, many definitions of white-collar crime exist that focus on different 

offenses and offenders. The National White Collar Crime Center's definition, for 

example, is far more inclusive: white-collar crime includes any "planned illegal or 

unethical acts of deception, committed by an individual or organization, usually 

during the course of legitimate occupational activity by persons of high or respectable 

status for personal or organizational gain that violates fiduciary responsibility or 

public trust" (M. Dodge, personal communication, January 30, 201 1). The labeling 

of white-collar crime has changed the way social scientists, economists, and 

businesses conduct and look at management and practices. 

Public sentiment appeared to be one of indifference before recent, high-profile 
white-collar crime scandals such as Enron's collapse, Martha Stewart's insider 
trading scandal, Bernard Madoff s Ponzi scheme, and WorldCom's dissolution. These 



1 



major white-collar crime incidents unleashed a frenzy of media attention that may 
have altered public ideas about the nature and seriousness of occupational and 
corporate crime. Changes in public perceptions about white-collar crime are the 
impetus for this research. This study is designed to examine public opinions about the 
seriousness of white-collar crime. In order to fully examine the public's perceptions 
of white-collar crime, this article examines prior literature on elite crime, presents 
original survey research, and lastly, explores policy implications and future research. 



2 



CHAPTER 2 
LITERATURE REVIEW 

When picturing a criminal, individuals rarely think of a man with his white- 
collar or a woman with her pearl necklace who sits in a position of corporate 
authority. Instead, individuals picture the hooded figures in the dark alleyway waiting 
to prey on their next victim. The stereotypical offender is perceived to be the 
"dangerous" street criminal. This common perception ignores professionals and 
corporate executives who engage in illegal and unethical behavior. In today's media 
savvy culture, scandalous information is rarely concealed for long periods of time. 
Daily the public will turn on their televisions, computers, or open a newspaper and 
read about an incident of white-collar crime. Despite recent publicity focusing on 
white-collar crime, few individuals actually understand the intricacies of the offenses 
and their impact on society. 

Media coverage often fails to fully explore the harms and costs of white-collar 
crime. The lack of understanding of the damages these elite criminals and crimes 
cause often shield upper echelon criminals from full blame and distort public 
perceptions. Victimization by white-collar offenders includes individuals and, in 
some cases, entire communities. In fact, tax payers often pay for the financial 
misdeeds of illegal actions by banks and government employees. Scholarly attention 
has resulted in a higher level of scrutiny of white-collar crime to help de- 



3 



mystifying the definition of white-collar crime, the costs of elite crime to society, and 
the malice behind these crimes. 

Unlike other areas of criminological research, white-collar crime is a fairly 
new area of study that has yet to be fully explored and researched. Due to white-collar 
crimes infancy in the scholarly field, research on the topic of elite crime is sparse and 
sporadic. Particularly, public perception of white-collar crime is underdeveloped in 
the literature. This literature review offers an overview of empirical explorations of 
white-collar crime and public perceptions, addresses issues of gender within white- 
collar crime, and examines public perception and sentencing. In sum, several white- 
collar crime issues emerge that deserve further research. First, current comparisons 
of current public perceptions of seriousness of white-collar crime versus traditional 
street crime are needed. Second, virtually no information is available that examines 
differences in perception between male and female offenders. Third, opinions about 
the mofivations and actions of the offenders have yet to be explored. Fourth, scant 
information exists about different views of appropriate levels of punishment for street 
versus white-collar crime. 

Public Perception Of White-Collar Crime 

" Although financial losses from white-collar crime continue to exceed those of street 
crime, the criminal justice system has traditionally focused on the latter" (Holtfreter, 

Van Slyke, Bratton, & Gertz, 2008, p. 50). 



4 



Within certain areas of criminology public perception research is vast, while 
in other areas, research is sparse. Perhaps the reason public perception on white-collar 
crime research is scant, is the lack of attention paid to elite deviance until more recent 
high profile scandals were featured in the media. Public perception of elite deviance 
is an area of research that has little literature available; specifically, opinions about 
the seriousness of white-collar crime. The various studies that have addressed the 
perceived seriousness of elite deviance have shown mixed results of just how the 
public perceives white-collar crime. 

Rossi, Waite, Bose, and Berk (1974) conducted one of the first studies on 
public perceptions of white-collar crime. Using a variety of crime scenarios, Rossi et 
al. noted several important discoveries about how individuals rank crime and the 
seriousness of various criminal acts. Crimes against persons were perceived as more 
serious than crimes where no harm against people was committed. White-collar 
crimes, which are not typically seen as being directly harmful to victims, were seen as 
less serious than other street crimes. 

Rossi et al. (1974) also discovered various demographic groups viewed crime 
differently. Their study found that Blacks tended to rate crime more seriously than 
Whites. Females viewed crimes more seriously than their male counterparts (Rossi et 
al., 1974). Younger individuals saw crime as a more serious issue than older 
individuals. Socio-economic status also influenced the ways individuals viewed the 



5 



crime scenarios; specifically, individuals from lower socio-economic households had 
higher seriousness ratings for the crimes overall compared to their higher socio- 
economic counterparts. The influence of socio-economic class was particularly 
noticeable with males. Lastly, education affected the way people viewed crime 
seriousness. Rossi et al. reported that the lower an individual's educational level, the 
more serious these individuals felt the crimes were, as opposed to the more highly 
educated individuals who viewed crime as less serious. Understanding how these 
various demographics affect individual's perception of crime seriousness opened the 
doors for scholars to understand how the public perceives issues differently based 
upon their various backgrounds. Rossi et al.'s study gave future scholars an initial 
framework for understanding how the public views white-collar crime. 

Rosenmerkel (2001) attempted to replicate Rossi's 1974 study measuring the 
seriousness of white-collar crime in relation to other crimes. Using a survey 
instrument, Rosenmerkel (2001) examined eight white-collar offenses, six property 
offenses, and seven violent offenses. He found that white-collar crime was rated as 
less serious than almost all other crimes; more specifically, elite deviance was 
believed to be less serious than property or violent crimes. However, when elite 
deviance was evaluated in the categories of harmfulness, seriousness, and 
wrongfulness white-collar crime was ranked between property and violent crime 
(Rosenmerkel, 2001). The study suggests that when ranking white-collar crime 
offenses individuals use harmfulness over the wrongfulness of the offense when 



6 



rating the seriousness of elite deviance. Lastly, the results showed that harm produced 
by the crime was more important than the seriousness when detennining the 
wrongfulness of the elite deviance. Unlike the Rossi et al. ( 1 974) study, 
Rosenmerkel's was unable to reproduce the same results in terms of their 
demographic, though the latter used college students as participants. 

While Rossi et al. (1974) and Rosenmerkel's (2001) studies found that the 
public did not perceive white-collar crime to be serious or as serious as other crimes, 
Piquero, Carmichael, and Piquero (2008) found mixed results that contradict the 
findings. In terms of seriousness, Piquero et al. discovered that elite deviance was 
perceived to be more serious in four of the six crime categories. When all six 
categories were compared, between 14 and 21 percent of the total sample respondents 
believed that elite deviance was as equally serious as the street crimes scenarios. 

In Piquero et al.'s (2008) study, results largely suggested that a majority of 
individuals perceived white-collar crime to be just as serious as street crime, if not 
more serious in some cases. This finding suggests that earlier views about white- 
collar crime being less serious are changing, and that possible recent attention to elite 
deviance by the media has raised awareness. When asked about how resources should 
be allocated, 65% of the respondents believed that an equal amount of resources 
should be spent on dealing with street crime and white-collar crime. Overall, it 
appears that the public is becoming less tolerant of elite deviance. 



Other notable variables in the Piquero et al. (2008) study included certain 
demographics that heavily influenced how individuals viewed the seriousness of 
white-collar crime; these demographic variables include age and education. Older 
individuals were more likely to see white-collar crimes as equally serious as the street 
crimes whereas younger individuals were less likely to see white-collar crime as 
being equally serious. Individuals who had college education also perceived white- 
collar crime to be equally serious to street crime. Piquero et al. suggest that 
individuals with college educations have more exposure to problems and costs 
associated with white-collar crime. Lastly, the researchers found that sex and marital 
status did not have as much influence on individual's perceptions of seriousness of 
street and white-collar crime as previously believed. 

While these studies offer some insight into the public's perception on white- 
collar crime, they offer few definitive answers. Since Rossi et al.'s (1974) 
groundbreaking study and Piquero et al.'s (2008) study, greater media and scholarly 
attention has been paid to elite deviance. Therefore, one might conclude that the extra 
media attention and empirical work has brought attention to the issue of elite 
deviance that did not exist prior to the 1970s. However, no definitive answers within 
the literature are offered as to the public attitudes shift in elite deviance, or even if the 
public's overall perception of white-collar crime has changed. 



8 



Gender and White-Collar Crime 

"White-collar crime has been researched by men and about men with little 
recognition of the roles women play as victims and perpetrators of elite deviance " 

(Dodge, 2009, p. I) 

Typically, the white-collar offender is a male who is educated and holds a 
position of authority or power within the corporate arena (Vande Walle, 2002). Most 
accepted definitions of white-collar crime are based upon the identity of the offender, 
who is still typically seen as a male. Dodge (2009) believes that "occupational 
marginality is the primary reason for the low number of women who participate in 
elite deviance, and low-status employment positions have circumscribed their efforts 
to engage in crimes associated with power and prestige" (p. 14). Today, women's role 
within elite deviance is evolving, and changing; "the role of women in white-collar 
crime has been discounted as crimes of the powerless, and the rare cases in which 
women held positions of authority and engaged in illegalities have limited definitive 
conclusions about their actions and the nature of their crimes," (Dodge, 2009, p. 24). 
Women's role in elite deviance will continue to evolve as their participation in the 
economic and corporate world expand. 

Historically, men have dominated the criminological research and the theories 
behind deviance; while women have typically been neglected fi-om research arenas 
involving criminality. When women have been acknowledged within criminological 



9 



research, they are seen as: biologically different, unnatural, abnormal, or crazy (see 
e.g., Cohen, 1955; Pollak, 1950). Historical assumptions about female criminality 
within criminological research have tainted gender perspectives, and served as a basis 
for patronizing and demeaning woman. The historical assumptions about female 
criminality created attitudes that women were unworthy of study. During the 1960s 
and 1970s, female criminality began to be looked at in a different lens by scholars, 
and regarded as something that warranted attention; the older theories about female 
criminals as inherently different were no longer seen as acceptable for explaining 
female criminality (Adler, 1975; Simon, 1975). While the 1960s and 1970s changed 
the way scholars were examining women's deviance, the old attitudes and outlooks of 
women's deviance were still prevalent. 

Feminist scholars have attempted to explain female deviance differently than 
their male counterparts (Cullen & Agnew, 2003; Daly & Chesney-Lind; 1988). Many 
feminist theories for deviance are shaped by the social movements that have 
transformed ideas about women's roles in society (Dodge, 2009). When discussing 
elite deviance, gendered terms are rarely used, since men have mostly dominated the 
corporate, economic, and political worlds. Dodge (2009) explains that the 
overrepresentation of males in elite deviance is tied to the position and opportunity 
available to men, which traditionally has not been available to women. Limited 
opportunity to engage in elite deviance has kept women out of the corporate, 
economic, and political arenas (Adler, 1975). 



10 



Though excluded in large part from these arenas, women have been 
victimized by white-collar crime. The victimization and vulnerability of women to 
white-collar crime is especially evident in the Victorian society; lack of knowledge 
about business matters and limited access to reliable sources of financial information 
left women highly vulnerable to abuses of white-collar crime during the \9^^ Century 
(Robb, 2006). Robb noted: "considerable evidence exists that women were sought out 
as victims by frauds and embezzlers who well understood their vulnerability" (p. 
1062). Shareholding was viewed by many women as an investment that offered them 
a way to make money, yet, according to Robb, this type of capital investment still left 
women open to abuses by the economic system. Throughout the 19^*^ Century, 
middle-class women organized movements to seek greater political participation, 
better educational opportunities, and economic independence from men (Robb, 2006). 

The first wave of feminist organizing the women's movements in England and 
America highly crificized the economic system that marginalized and left women 
vulnerable to exploitations (Robb, 2006). Feminists looking to change women's 
posifions and opportunities within society were met with fierce opposition from 
tradifionalist who countered that allowing females into the foundations of society 
would destroy the purity of women by exposing women to the corrupfion of the 
market (Robb, 2006). The discourse of women's appropriate place in society and 
exclusion from the marketplace continued throughout the Victorian era; according to 
Robb, it was feared that by allowing women "outside the protective, or restraining. 



11 



influences of the home, women might prove even more reckless than men" (p. 1066). 
The view that women would be corrupted by the economic sector held for a large 
portion of the 19th century. 

For centuries, women continued to struggle for equality in the economic 
sector. It was not until the passage of the Equal Protection Clause of the Fourteenth 
Amendment, and the 1964 Civil Rights Act that women started entering the 
workforce in mass numbers, and in positions that were equal to their male 
counterparts (Dodge, 2009). In 1972, the Equal Employment Opportunity Act as 
applied to Title VII of the Civil Rights Act passed, which tore down the social, and 
legal structures that once allowed discrimination to occur based on sex, religion, race, 
and national origin within a workplace. As women entered the workforce, female 
scholars began looking at the expanded opportunities for women in the economic 
sector and how criminal activity might change (Adler, 1975; Simon, 1975). 

Freda Adler (1975) began exploring the issues of female criminality as a 
whole, and female criminality within white-collar crime. Adler argued that, "the 
higher rates for female deviancy was based on increased opportunities and decreased 
social controls" (Dodge, 2009, p. 10). These decreased social control and increased 
opportunities allowed women to compete with their male counterparts; however, with 
these opportunities came the increased opportunity for women to engage in the 
criminality similar to their male counterparts. Adler was not the only feminist scholar 



12 



to predict the rise in female criminality. Rita James Simon (1975) also predicted that 
as opportunities arose, women would become involved in white-collar crime. While 
there was some consensus among feminist scholars that as women's opportunities 
increase so would their deviance, there was controversy as to where females fit into 
the male dominated world of white-collar crime. 

Public Perception on Sentencing 

''In the end, the public shows a tendency to be punitive and progressive, wishing the 
correctional system to achieve the diverse missions of doing justice, protecting public 
safety, and reforming the wayward" (Cullen, Fisher, and Apple gate, 2000, p. I). 

It is important to address the issue of public perceptions on sentencing. An 
examination of prior literature on the public perceptions of appropriate sentences for 
elite and street deviants provides a framework for understanding public viewpoints on 
street and white-collar crime. Prior research on punishment delineates two distinct 
categories: demographic factors that influence sentencing opinions and issues on 
sanctioning white-collar offenders versus street crimes. 

Demographic Factors that Influence Sentencing Options: 

Holtfreter, Van Slyke, Bratton, and Gertz (2008) specifically addressed public 
perceptions about apprehending and punishing white-collar offenders and street 
criminals. Holtfreter et al. found that a majority of respondents felt that violent 



13 



offenders were more likely to be caught and receive harsher punishments for their 
crimes compared to white-collar criminals. They also found that there were various 
demographics that affected the way members of the public perceived sentencing of 
offenders. Gender of respondents affected how individuals felt about the sentencing 
of various types of offenders. Females were significantly more likely to believe that 
white-collar criminals had an equal or greater chance of being caught and receiving a 
harsher punishment if caught than street criminals. In addition, females were 
significantly less likely to support funding for fighting white-collar crime compared 
to street crime. Similarly, Schoepfer, Carmichael, and Piquero (2007) found that 
females were less likely to believe that street crime should be punished more severely 
than white-collar crime. 

Income represented another significant predictor as to how individuals viewed 
the likelihood of an individual being apprehended and punished for white-collar 
crime. Individuals who made over $50,000 annually were significantly less likely to 
believe that elite deviants would be caught and punished for their misdeeds 
(Holtfreter et al., 2008). Income paralleled with education in punishment and 
apprehension beliefs, individuals with more education were less likely to believe that 
elite deviants would be caught and apprehended for their crimes (Holtfreter et al., 
2008; Schoepfer et al., 2007). However, Schoepfer et al., (2007) found that 
individuals in higher income and educational brackets were less likely to believe that 
white-collar criminals would receive a more severe punishment when apprehended. 

14 



Ideology also played a role in how individuals perceived the likelihood of elite 
deviance being apprehended and punished for their crimes. Individuals who identified 
themselves as conservative or moderate were more likely than individuals who 
identified themselves as liberal to believe that white-collar offenders had an equal or 
greater chance of being punished (Holtfreter et al., 2008). However, when examining 
Schoepfer et al.'s (2007) study, conservatives were less likely to believe robbery and 
the white-collar crime of fraud, would be equally likely to receive harsh punishments. 
While conservative to moderate individuals believed in punishing white-collar 
criminals, there was some disagreement as to whether political ideology influences 
harsher punishment. 

Geographic location also influenced whether or not individuals perceived elite 
deviance as receiving punishment. Holtfreter et al. (2008) found that individuals who 
lived in urban settings were less likely to believe that white-collar criminals would be 
sanctioned. In contrast, Schoepfer et al. (2007) found that city dwellers were more 
likely to believe that white-collar criminals should be punished more severely than to 
believe that street crimes and white-collar crimes should be equally punished. In 
addition to geographic location, homeownership also influenced whether or not 
individuals believed elite deviants would be punished. Overall, homeowners were 
more likely to want elite deviants punished at an equal or harsher level to violent 
offenders (Holtfreter et al., 2008). Lastly, race was found to have little to no effect on 



15 



the opinions about whether or not eUte deviants should be punished more harshly than 
street criminals. 

Issues Sanctioning White-Collar Criminals 

In the past, the general public has failed to pay attention to white-collar crime; 
henceforth, a major attitude shift needed to occur for the public to become favorable 
towards prosecuting elite deviants. Three distinct chronological shifts occurred within 
the public mindset that changed support for punishing white-collar criminals (Cullen, 
Hartman, & Jonson, 2009). The first chronological period is referred to as the 
inattention period; this time is often referred to as any decade prior to the 1970s 
(Cullen et al., 2009). During the inattention period, the public was unaware of the 
dangers of elite deviance, political power was able to deflect criminal law, and 
company officials power and authority prevented offenders from being seen as a 
common criminal (Cullen et al., 2009). Characteristic of this period, according to 
Cullen et al., was how white-collar criminals lived well beyond the means of the 
criminal law and prosecution of these high powered individuals was unattainable. The 
inattention period was also characterized by public ignorance and apathy towards 
upper world criminality; without the public's intimate knowledge about the dangers 
of elite deviance, white-collar criminals were able to continue their deviance without 
uproar from the general public (Cullen et al., 2009). During the 1970s attitudes within 
the public began to shift. 



16 



The second period is labeled the rising attention period. After 1970s, attention 



began to focus on white-collar crime. The Civil Rights movement of the 1960s 
focused on equal justice for all, it was through the movements for equal justice that 
elite deviants were suddenly not seen as untouchable by the justice system (Cullen et 
al., 2009). During the rising attention period, elite deviance became a common term 
in magazines, newspapers, and was reported on by the media (Cullen et al., 2009). 
The attention period brought around a significant shift in the public attitude, white- 
collar criminals were now being seen as criminals rather than elites in society, and 
worthy of punishment. 

The final period is denoted as the "bad guy period." Since 2000, white-collar 
criminals have been labeled as "bad guys," and deserving of punishment (Cullen et 
al, 2009). Cullen et al. (2009) noted: 

As its prevalence and the magnitude of its harm was publicized, the 
public became aware of white-collar crime and critical of offenders in 
white-collars. Confidence in businesses and in other institufions 
declined, while concern for equal justice escalated. This confluence 
created a special problem for the government. For the state to protect 
its own declining legitimacy, it had to show a concerned public that it 
was not beholden to corporate interests. It had to prove that it 
understood the need for victims to be accorded "total justice." As a 
result, the state created space for the expanded use of the criminal law 
against white-collar miscreants. In doing so, it revealed that crime 
occurred across classes and that no offender was above the law (p. 38). 

The shift in labeling white-collar criminals as "bad guys" came from a large public 
push that labeled these elite offenders as criminals and not above the law. 



17 



Currently, more attention is paid to white-collar criminals. Previous research 
has examined the area of public perception of sentencing elite deviants and suggests 
that the public favors harsh sentencing of white-collar offenders. Some research even 
suggests that the public feels that white-collar criminals should be punished as 
severely as street criminals. Understanding public opinion on sentencing of white- 
collar offenders offers insight into whether or not elite deviance is viewed as a serious 
issue. 

One research study found that over 80 percent of the subjects felt that white- 
collar criminals had been treated too leniently and needed to be punished just as 
severely as street criminals (Cullen, Mathers, Clark, & Cullen, 1983). Certain types of 
white-collar criminality were more likely to be seen as serious, specifically if the 
offense involved physical harm or individual violations of trust that would normally 
be disapproved by society. However, participants were less troubled by the issue of 
price fixing, false advertising, or other types of corporate illegalities. Despite that the 
public was not as bothered by corporate illegalities as they were white-collar crimes 
that showed direct harm, subjects were willing to still hand out criminal penalties to 
white-collar offenders, even if the crimes were deemed relatively minor. Cullen et al. 
(1983) found that 73 percent of the subjects believe that stiffer jail sentences may 
make white-collar criminals re-think their calculated efforts to commit deviance. The 
researchers concluded that subjects felt elite deviants were being treated too leniently 
by the justice system and should pay more harshly for their crimes. 



18 



Outside of formal sanctions from the judicial system, there are informal 
sanctions. Benson (1989) speculated that class position influences both the formal and 
informal sanctions that occur when white-collar criminals are sentenced in and out of 
the courtroom. Depending on the individual's social standing, there is certain stigma 
attached to the sentence. Benson speculated that for less powerful white-collar 
offenders there is more of a stigma attached to their criminal sentence than more 
powerful elite deviant. Overall, social class determined the informal sanctions 
individual elite offenders received; according to Bensen, elite working class offenders 
are more likely to suffer from their conviction than those in a higher social standing. 
The legal stigma handed down during convictions is more likely to discredit workers 
and lower-class elite criminals than higher authority criminals (Benson, 1989). In 
addition, although higher elite deviants commit some of the most serious offenses, 
they are the least likely to lose their jobs or positions of trust because of a criminal 
conviction. Benson concluded that informal sanctioning may be more influenced by 
class structures than the social control of the law. Individuals of higher social 
standing are more likely to receive less social stigma for their deviance than their 
lower social standing counterparts who are more susceptible to the social stigma that 
comes from a formal or criminal conviction. 

Unlike other scholars, Podgor (2007) presents a different viewpoint on the 
challenges of sentencing white-collar offenders. She maintains that the typical 
white-collar offender is likely to receive more prison time than an individual who has 



19 



committed a violent crime; in addition, most of tliese elite deviants are first time 
offenders. She argues that since elite deviants have further to fall, they stand to lose 
more from their criminal conviction. Lastly, Podgor contends that individuals who 
have been sentenced and convicted of white-collar crime can seldom return to the 
jobs or positions of authority. Consequently, recidivism rates are fairly low compared 
to other crimes. While Podgor' s argument is interesting, some may view it as being 
sympathetic to the elite deviants who often cause more damage to society than a basic 
street criminal. However, the opposing viewpoints are important in order to fully 
understand the challenges of sentencing white-collar criminals. 



20 



CHAPTER 3 
RESEARCH HYPOTHESIS 

The current research project is designed to explore pubHc perceptions of 

white-collar crime versus street crime. Of focus is the role that offenders' gender 

plays in these perceptions. To study this, several dependent variables are used: 

perceptions of seriousness, punishment, greed, remorse, and stress. The research also 

explores perceptions of motivations and offender behavior. The null hypotheses and 

research hypotheses are presented below: 

Hq: Perceptions of white-collar versus street crime are consistent for male and 
female offenders. 

Hi: People view crimes by females as more serious compared to offenses 
committed by males. 



21 



CHAPTER FOUR 
METHODOLOGY 

Participants 

Data were collected in El Paso County, Colorado between February 7 and 
February 28, 2011 . Participants were individuals who had been summoned for jury 
duty. Nine-hundred individuals volunteered to complete a survey and were informed 
of their rights as research participants. Participants completed a two-page survey, 
which asked about their opinions on one of 12 crime scenarios. All participants took 
part in the study after they had been dismissed from jury duty to avoid influencing the 
outcomes of cases on the court docket. In addition, participants were given a brief 
definition of white-collar crime, which can be seen as a limitation to the results of this 
current study. Lastly, due to the researcher being a White female, these results may 
have come out differently if the researcher identified as another sex and racial/ethnic 
identity. 

Survey Instrument 

The survey contained 13 questions (see Appendix A). There were 12 different 
crime scenaiios. Six original scenarios were created, three scenarios involved street 
crimes and three involved white-collar crime committed by either a male or female 



22 



offender. All crimes within the survey instruments involved no direct harm to the 
scenario victims. The researcher chose these various crimes since there was no direct 
human harm to the participants to better survey whether or not research participants 
saw white-collar crime or street crime as more serious. 

Following each scenario, five questions explored public perceptions of 
elements of the crime scenarios. Respondents were asked to rate the seriousness of 
the crime, type of punishment deserved for the crime committed, the remorse felt by 
the defendant, how much greed was responsible for his/her actions, and to what 
extent stress was responsible for the individual committing their crime. Eight 
demographic questions asked participants to identify: if they had been a victim of 
white-collar crime, if they had been a victim of street crime, their gender, their 
race/ethnicity, age in years, highest level of education attained, current marital status, 
and current employment status. 

Street Crimes 

As previously stated, part of this research design was to measure public's 
perception of the seriousness of common street crimes. The goal of the survey 
instrument was to make the street crimes used in the vignettes understandable to the 
general population, and to ensure that the types of crimes were common enough that 
the general population would comprehend what the vignettes were asking. The street 
crime scenarios included auto theft, burglary, and prostitution. 



23 



Research subjects were asked to evaluate the following scenarios: 

For the last ten years, Bob Wilson/ Jane Wilson, has been unemployed, 
and has supported himself/herself by stealing cars. Bob/Jane targets 
cars that have high Blue Book value and expensive unattended items in 
plain view. The police catch Bob/Jane in the act of auto theft, and 
charge him/her with auto theft. In order to avoid a trial he/she pleads 
guilty to auto theft charges. 



For the last ten years. Bob Wilson/Jane Wilson, has been unemployed, 
and has supported himself/herself by burglarizing homes. Bob/Jane 
targets houses that are unoccupied and steals money, jewelry, and 
electronics. The police catch Bob/ Jane in the act and he/she is charged 
with burglary. To avoid a trail he/she confesses to the burglary. 

For the last ten years Bob Wilson/Jane Wilson has been unemployed 
and has supported himself/herself as a prostitute. The police do a sting 
on the area of town which is known for prostitution, and catch 
Bob/Jane engaging in prostitution. In order to avoid a trail he/she 
pleads guilty to prostitution charges. 

Once survey respondents finished reading the scenario, they were asked to rate 
seriousness of the crime on an eight-point Likert scale, where 1 = not very serious and 
8 = extremely serious. 

The second question explored the punishment they felt the offender deserved. 
The response categories for this question were closed ended and included: probation, 
monetary fine, jail, prison, and other. If respondents chose "other," they were asked to 
specify their ideas about punishment. Respondents were asked to rate how much 
remorse, greed, and stress they believed the offender felt on an eight-point Likert 
scale, where 1 = "not very" and 8 = "extremely." 



24 



White-Collar Crimes 



The goal of this research design is to measure the pubHc's perception of the 
seriousness of white-collar crime compared to street crime. The three white-collar 
crime scenarios involved corporate crime, embezzlement, and a Ponzi scheme. 
Except for embezzlement, the white-collar crime scenarios were modeled after recent 
high profile white-collar crimes that featured in the media. The Ponzi scheme closely 
mirrored the case of Bernard Madoff s Ponzi scheme that cheated people out of 
millions of dollars. The corporate crime scenario used in the survey instrument was 
modeled after the Enron scandal. 

Respondents were asked to evaluate the following scenarios: 

For the past ten years, Bob/ Jane Wilson has worked as CEO for a 
multibillion-dollar energy exploration company. During the last five 
years, the company began counting money that had not been collected 
fi-om clients as revenue. These actions allowed the company to inflate 
their profits by $250 million. In order to avoid a trail, Bob/Jane 
confesses to engaging in corporate crime. 

For the last ten years, Bob/Jane Wilson has worked for a legal firm as 
an office manager where he/she handles the firm 's monetary 
transactions. For the last five years, Bob/Jane has taken money from 
the bank deposits. The law firm started an investigation, and found 
money was missing. Bob/Jane was arrested and charged with 
embezzlement. To avoid a trial he/she confessed to the embezzlement. 

For the past ten years, Jane/Bob Wilson has worked as a financial 
advisor where she/he advised her/his clients to put their money into 
her/his new Zoom IV account. Jane/Bob uses her/his new investor 's 
money to pay off' her/his old investors in this Zoom IV account in this 
large Ponzi scheme. She/He has also stolen large amounts of money 
directly from her/his clients as well as the business accounts. The 



25 



police investigate her/his financial firm regarding the missing 
financial funds and conclude that Jane/Bob took money from her his 
investors. In order to avoid a trail she/he confesses to the Ponzi 
scheme. 

Demographics 

The last section on the survey instrument included eight demographic 
questions. These questions asked if the sur\ ey respondent has been victimized by 
white-collar crime, if the survey respondent has been a victim of a street or white- 
collar crime, gender, race, age. highest level of education achieved, current marital 
status, and current employment status. Every demographic except for age. had 
designated response categories which were designed to be mutually exclusive and 
exhaustive. 

The five racial categories on the survey instrument included: Black. Hispanic. 
White, Asian, and other; the other category had an open-ended response space where 
individuals could fill in the racial category that they identify as in a more specific 
manner. Age was the only category that did not have designated response categories 
for the respondents to choose from: instead, participants were asked to identify their 
exact age in years in the open-ended response category. Respondents were asked to 
choose which option best described their highest level of education completed using 
an ordinal measure: less than an eighth grade education, a high school diploma or 
GED, some college but no degree, four-year degree or a Bachelor's, a Master's 
degree, an Advanced degree (Ph.D.. or .I.D.). 



26 



The final two demographic questions aslced about marital and employment 
status. For marital status, respondents were asked to choose from the following closed 
response categories: married, never married, widowed, divorced, and separated. 
Lastly, respondents were asked to identify their current employment status. 
Respondents were given two closed ended response categories to choose from: 
unemployed or employed. While the employment status options did not include 
retired, retired individuals were considered by the researcher to be unemployed since 
they are not currently participating in the workforce. 

Analytic Technique 

Two analytic techniques were used to address the research questions. The first 
analytic technique, ordinary least squares regression (OLS) was performed on the 
four independent variables of Seriousness, Remorse, Greed, and Stress. OLS is 
appropriate for these regressions given that the dependent variables noted are 
continuous in nature. The second analytic technique, binary logistical regression is 
used for the final dependent variables given they are binary. Punishment was divided 
into two categories severe punishment (jail/prison) or not severe punishment 
(monetary fine/probation). 



27 



CHAPTER 5 
RESULTS 

Before addressing the research question, this section describes the sample. 
Table one presents descriptive statistics for all dependent variables used in the 
analysis. Results show that with one exception, respondents viewed the selected 
crimes as very serious. The exception to this was prostitution which was seen as less 
than moderately serious. Findings also demonstrate that respondents perceived that 
offenders in the scenarios felt very little remorse for the crimes portrayed. 



28 



Table 1 : Descriptive Statistics for the dependent variables (Ponzi Scheme, 

Embezzlement, Auto Theft) 



Ponzi Scheme 






Embezzlement 




Auto Theft 






Seriousness of crime 






Seriousness of crime 




Seriousness of crime 






Mean 


7.05 








Mean 


6.70 






Mean 


6.33 






Standard 
deviation 


1.06 








Standard 
deviation 


1.30 






Standard 
deviation 


1.37 




Remorse of offender 






Remorse of offender 




Remorse of offender 






Mean 


2.92 








Mean 


3.76 






Mean 


2.70 






Standard 
deviation 


1.89 








Standard 
deviation 


1.80 






Standard 
deviation 


1.65 




Greed of offender 






Greed of offender 






Greed of offender 








Mean 


7.15 








Mean 


6.30 






Mean 


5.50 






Standard 
deviation 


1.20 








Standard 
deviation 


1.64 






Standard 
deviation 


2.16 




Stress of offender 






Stress of offender 




Stress of offender 






Mean 


4.54 








Mean 


4.54 






Mean 


4.49 






Standard 
deviation 


2.30 








Standard 
deviation 


2.30 






Standard 
deviation 


2.19 




Punishment for offender 






Punishment for offender 




Punishment for offender 






Severe 
punishment 


92.0% 








Severe 
punishment 


76.1% 






Severe 
punishment 


87,6% 






Less severe 
punishment 


8.0% 








Less severe 
punishment 


23.9% 






Less severe 
punishment 


12.4% 




























Sea 


es for Seriousness, Remorse, Greed, and Stress were measured using an 8 point Likert Scale 


1= Not very 






















8- Extremely.... 






















Punishment was coded as a binary variable (0 and 1) 










0= Less Serious Punishment 


















1= Severe Punishment 





















29 



Table 2: Descriptive Statistics for the dependent variables (Burglary, Corporate 

Crime, and Prostitution) 



Burglary 




Corporate Crime | 


Prostitution 






Seriousness of crime 




Seriousness of crime 






Seriousness of crime 






Mean 


6.48 






Mean 


6.45 








Mean 


4.77 






Standard 
deviation 


1.23 






Standard 
deviation 


1.53 








Standard 
deviation 


1.92 




Remorse of offender 




Remorse of offender 






Remorse of offender 






Mean 


3.00 






Mean 


3.26 








Mean 


3.13 






Standard 
deviation 


L80 






Standard 
deviation 


1.81 








Standard 
deviation 


1.81 




Greed of offender 






Greed of offender 








Greed of offender 








Mean 


5.36 






Mean 


6.74 








Mean 


3.42 






Standard 
deviation 


2.02 






Standard 
deviation 


1.54 








Standard 
deviation 


2.02 




Stress of offender 






Stress of offender 








Stress of offender 








Mean 


4.53 






Mean 


4.42 








Mean 


5.01 






Standard 
deviation 


2.14 






Standard 
deviation 


2.10 








Standard 
deviation 


2.15 




Punishment for offender 




Punishment for offender 






Punishment for offender 






Severe 
punishment 


90.1% 






Severe 
punishment 


69.5% 








Severe 
punishment 


29.3% 






Less severe 
punishment 


9,9% 






Less severe 
punishment 


30.5% 








Less severe 
punishment 


70.7% 




























Seal 


es for Seriousness, Remorse, Greed, and Stress were measured using an 8 point Likert Scale 


1= Not very 






















8^ Extremely.... 






















Punishment was codec 


as a binary variable (0 and 1) 












0= Less Serious Punishment 




















1= Severe Punishment 





















The next table offers descriptive for all other variables in the analysis. About 
half of the respondents were female (53.2%), most were white (81.6%), most were 
characterized by some college but no degree (35%). Further, most were married 



30 



(63.4%), middle aged (mean age of 45 years old) and employed (74.0%). The next 
section focuses on the research questions using regression analysis. 



Table 3: Descriptive statistics, percentage, means and standard deviations for sample, 

n=900 



Independent variable 






Employ 


ment status 






Gender of respondent 








Unemployed 


26.0% 






Male 








Employed 


74.0% 






Female 


53 2% 
























Victim of white collar crime in the past 


Respondent Characteristics 








Yes 




16.2% ^ 




Race of Respondent 




















Black 


5.1% 




Victim of street crime in the past 






Hispanic (any race) 


8.5% 






Yes 


25.3% 






White 


81.6% 


















Asian 


1.8% 




Ase of Respondent 








Other 


3.0% 






Mean 


45.41 














Standard deviation 


14.80 




Education level of Respondent 




















Less than 8th grade 


0.5% 




Marital status of Respondent 








High school diploma or GED 


16.7% 






Married 


63.4% 






Some college, no degree 


35.0% 






Never married 


18.4% 






Bachelor's degree 


28.0% 






Widowed 


1.8% 






Master's degree 


15.7% 






Divorced 


14.2% 






Advanced degree 


4.1% 






Separated 


2.2% 



Ordinary Least Squares Analyses 

Ordinary Least Squares Regression was employed for the initial models. 
Specifically, this section offers findings for the dependent variables of seriousness, 
remorse, greed, and stress. The findings for crimes are offered in the following order: 
ponzi schemes, embezzlement, auto theft, burglary, corporate crime and prostitution. 



31 



Ponzi Crimes 

Seriousness as the Dependent Variable 

The first regression focused on Ponzi schemes used "seriousness" as the 
dependent variable. An OLS regression addressing this relationship indicates several 
significant findings. First, the research hypothesis was supported as male respondents 
were significantly more likely to view a Ponzi scheme as more serious than were 
female respondents (b=.185, p=.093). Respondents did not view gender of the 
offender as a significant predictor of the seriousness of the Ponzi scheme (b=.270, 
p=.137). The model is characterized by a good fit as 19.2 percent of the variation in 
seriousness is explained for by the variables. 

Remorse as the Dependent Variable 

The next regression addressing Ponzi schemes used "remorse" as the 
dependent variable. An OLS regression addressing this dependent variable indicates 
that respondent's gender is not a significant predictor of whether individuals viewed 
the remorse feh by the criminal (b=.420, p=.l 1 1). In contrast, the offender's gender 
was a significant predictor of whether the public viewed a Ponzi criminal as 
remorseful. Specifically, male offenders were thought to be more remorseful than 
female offenders (b=.032, p=.009). The model is characterized by a good fit as 26.7 
percent of the variation in remorsefulness is explained for by the variables. 



32 



Greed as {he Depeiuleiit Variah/e 

The next regression addressing Ponzi schemes used "greed" as the dependent 
variable. An OI.S regression addressing this dependent variable indicates that 
respondent's gender is not a significant predictor of how individuals viewed greed of 
criminal (b=.147. p-=.452). In contrast, the offender's gender imv a significant 
predictor of whether the public viewed a Ponzi criminal as greedy. Specifically, male 
offenders were thought to be less greedy than female offenders (b= -.319, p=.090). 
The model is characterized by a good fit as 22.7 percent of the variation in greediness 
is explained for by the variables. 

Stress as the DepenclenI I 'ariahle 

The next regression addressing Ponzi schemes used "stress" as the dependent 
variable. An OLS regression addressing this dependent variable indicates that 
respondent's gender is not a significant predictor of how individuals viewed stress of 
criminal (b=.395, p=.3 1 3). In addition, the offender's gender was not a significant 
predictor of whether the public viewed the scenarios Ponzi criminal's actions as stress 
related. Male offenders were thought to be no more stress induced than female 
offenders (b=.294, p=.470). The model is characterized by a good fit as 23.6 percent 
of the variation in stress is explained for by the variables. 



33 



Table 4: Regression analyses for Ponzi crimes 











Ordinary Least Squares (OLS) 










Seriousness 


Remorse 


Greed 


Stress 


Variables 


b 


SE 




value 


Beta 


b 


SE 




W— 

V 

value 


Beta 


b 


SE 




V 

value 


Beta 


b 


SE 




n- 

value 


[Beta 


















































Independent Variable 












































Gender of Respondent 














































Male 


0.185 


0.170 


+ 


0.093 


0.279 


0,420 


0.313 




O.lll 


0.182 


0.147 


0.195 




0.452 


0.066 


.395 


.390 




.313 


.085 


Offender's Gender 












































Male offender 


0.270 


0.177 




0.137 


0.131 


0.032 


0.326 


* 


0.009 


0.921 


-0.319 


0.187 




0.090 


-0.143 


.294 


.406 




.470 


.064 


Respondent Characteristic 












































Marital Status 














































Never married 




U.24 / 




-0. lUo 


A O 


-U. 126 


0.474 




A A'^ z; 

-0.026 


A -7A 1 

0,791 


-0.417 


0.271 




0. 127 


A 1 .,'t 

-0, 148 


1 T 

.333 


.job 




ceo 


A V 7 






Widowed 


U. / J I 




+ 


A AA< 


f\ A Al 

U.44 / 


A y 


1 -7 t> 




(J.U tz 


A Q A 
U.06D 


A "2 AA 


1 AO A 




A 7-7 < 

U, / / J 


A AT ^ 


Z.UVV 


Z.ZJZ 




1/1 
. j4V 


A7Q 






Divorce 


-U.UjU 


A /I ^ 




A A 1 


A /I 1 

(J.o4 1 


A 1 C 
(J. J JO 


U.4 jV 


-!- 


A A/LO 

O.Uoo 


A /I /I A 


A 1 1 1 
U. 1 1 1 


A TA 
U.Z /U 




A 

(J.ooz 


A A 


1 C A7 


<; A 




AA< 

.UU J 


.Zj4 






Separated 


U. / J / 


A T A 




A 1 1A 


A 1 <; 


U.OO / 


A A<A 




A AA. 1 

U.Uo 1 


A /I 00 


A s n 1 


A C77 




A K 


A AO 1 


Z.OO / 


1 1 QO 

1 . 1 Vz 


* 


A 1 7 


1 1 <C 
.Z I J 




Employed 


0.192 


0.215 


+ 


0.079 


0.375 


-0.864 


0.393 




-0.189 


0,030 


-0.215 


0.236 




0.365 


-0.079 


-.737 


.489 




.135 


-.131 




Victim of White-Collar Crime 


0.000 


0.000 




0.006 


0.946 


0.000 


0.000 


* 


0.008 


0,921 


0.00 


0.000 




0,130 


-0.128 


.000 


.000 




.836 


.018 




Via 


0.078 


CJ86 






0.675 


-0.771 


0.346 




-0.190 


0,028 


0.028 


0.204 




0.892 


0.012 


606 


.426 




.157 


-.123 




Race 
















































Black 


-0.591 


A /I C 1 

0.45 1 




A 1 1 T 

-0. 1 13 


0. 192 


-0.572 


0.8 19 




-0.059 


0.486 


-0.625 


0.495 




0.209 


-0. 106 


.998 


1 A'^ T 




.33 1 


A 01 

.Uo3 






Hispanic 


0.294 


0.308 


+ 


0.081 


0.341 


1.344 


0.560 




0.199 


0.018 


-0.538 


0.338 




0.114 


-0.133 


.936 


.698 




.183 


.112 






Asian 


0.079 


0.703 


* 


0.010 


0.910 


1.230 


1.278 




0.081 


0.338 


-0.563 


0.771 




0.467 


-0.061 


1.522 


1.596 




.342 


.081 






Other Race 


-0.161 


0.692 


* 


-0.020 


0.817 


4.089 


1.256 




0.268 


0.001 


-2.889 


0.760 


* 


0.000 


-0.314 


1.077 


1.569 




.494 


.057 




Education 


0.027 


0.077 


* 


0.031 


0.730 


-0.012 


0.143 


* 


-0.007 


0.934 


-0.147 


0.085 




0.086 


-0.153 


.172 


.177 




.332 


.087 




Age 




0.021 


0.007 




0.301 


0.003 


-0.041 


0.013 




-0.318 


0.002 


0.004 


0.008 




0.634 


0.047 


-.048 


.016 


* 


.003 


-.301 


Constant 


5.081 


0.645 






0.000 


5,669 


1.203 






0.000 


8.403 


0.708 




0.000 




6.003 


1.474 




.000 




















































* p<.05 




R-Squared-.192;SEE.943 


R-Squared - .267; SEE 1.710 


R-Squared = .227;SEE 1.035 


R-Squared= .236; SEE 2.137 


+ p<.10 




1 

1 1 


! 1 
1 1 




















i 

1 













34 



Embezzlement 

Seriousness as the Dependent Variable 

The first regression focused on embezzlement used "seriousness" as the 
dependent variable. An OLS regression addressing this relationship indicates several 
significant findings. First, the research hypothesis was not supported as male 
respondents were not significantly more likely to view embezzlement as more serious 
than were female respondents (b=.006, p=.979). Respondents did not view gender of 
the offender as a significant predictor of the seriousness of the embezzlement 
(b=.090, p=.667). The model is characterized by a poor fit as 10.6 percent of the 
variation in seriousness is explained for by the variables. 

Remorse as the Dependent Variable 

The second regression addressing embezzlement used "remorse" as the 
dependent variable. An OLS regression addressing this dependent variable indicates 
that respondent's gender is not a significant predictor of whether individuals viewed 
remorse of criminal (b= -.538, p=.103). Offender's gender was not a significant 
predictor of whether the public viewed an embezzler's as remorseful. Male 
embezzlers were thought to be no more remorseful than female embezzlers (b= -.426, 
p=.173). The model is characterized by a good fit as 22.2 percent of the variation in 
remorsefulness is explained for by the variables. 



35 



Greed as the Dependent Variable 

The third regression addressing embezzlement used "greed" as the dependent 
variable. An OLS regression addressing this dependent variable indicates that 
respondent's gender is not a significant predictor of how individuals viewed the greed 
of embezzlers (b=-.300, p=.320). Offender's gender was not a significant predictor of 
whether the public viewed an embezzler as greedy. Specifically, male embezzlers 
were thought to be no more greedy than female embezzlers (b= -.163, p=.570). The 
model is characterized by a fair fit as 14.8 percent of the variation in greediness is 
explained for by the variables. 

Stress as the Dependent Variable 

The last regression addressing embezzlement used "stress" as the dependent 
variable. An OLS regression addressing this dependent variable indicates that 
respondent's gender is not a significant predictor of how individuals viewed the 
stress of the embezzlers (b=.096, p=.804). In addition, the offender's gender was not 
a significant predictor of whether the public viewed the embezzler's actions as stress 
related. Male offenders were thought to be no more stress induced than female 
offenders (b= -.502, p=.217). The model is characterized by a poor fit as 9 percent of 
the variation in stress is explained for by the variables. 



36 



Table 5: Regression analyses for Embezzlement crimes 











Ordinary Least Squares (OLS) 










Seriousness 


Remorse 


Greed 


Stress 


Variables 


b 


SE 




value 


Beta 


b 


SE 




D- 

value 


Beta 


B 


SE 




n- 
value 


Beta 


b 


SE 




n- 
value 


Beta 


Independent Variable 












































Gender of Respondent 














































Male 


.006 


.217 




.979 


.002 


-.538 


.327 




.103 


-.148 


-.300 


.300 




.320 


-.093 


.096 


.383 




.804 


.023 


Offender's Gender 












































Male offender 


.090 


.208 




.667 


.038 


-.426 


.311 




.173 


-.117 


-.163 


.286 




.570 


-.051 


-.502 


.404 




.217 


-.121 


Respondent Characteristic 












































Marital Status 














































Never married 


-.169 


.289 




.561 


-.058 


.258 


.422 




.543 


.058 


-.184 


.401 




.647 


-.045 


-.164 


.532 




.758 


-.032 






Widowed 


1.119 


.859 




.195 


.115 


-2.869 


1.256 


* 


.024 


-.194 


-.895 


1.165 




.444 


-.068 


-.218 


1.541 




.888 


-.013 






Divorce 


.041 


.319 




.897 


.012 


.454 


.497 




.363 


.082 


-.478 


.461 




.301 


-.097 


.249 


.609 




.684 


.040 






Separated 


-.572 


1.207 




.637 


-.042 


-1.166 


1.763 




.510 


-.056 


1.876 


1.637 




.254 


.101 


-3.683 


2.163 


+ 


.091 


-.157 




Employed 


.182 


.251 




.471 


.067 


-.794 


.376 


* 


.037 


-.190 


.142 


.346 




.682 


.038 


-.048 


.459 




.916 


-.010 




Victim of White-CoUar Crime 


.000 


.000 




.391 


.080 


.000 


.000 




.820 


-.020 


.000 


.000 




.852 


-.017 


.000 


.000 




.300 


.101 




Victim of Street Crime 


.000 


.000 




.664 


.039 


.000 


.000 




.366 


.078 


.000 


.000 




.296 


.094 


.000 


.000 




.709 


-.035 




Race 
















































Black 


-.909 


.524 




.085 


-.172 


.694 


.773 




.371 


.086 


1.755 


.718 


* 


.016 


-.244 


-.396 


.952 




.678 


-.044 






Hispanic 


.463 


.322 




.153 


.131 


.489 


.471 




.302 


.091 


-.173 


.437 




.694 


-.036 


.566 


.579 




.330 


.093 






Asian 


-.434 


.715 




.545 


-.054 


1.284 


1.049 




.223 


.106 


.204 


.971 




.834 


.019 


-.092 


1.285 




.943 


-.007 






Other Race 


-.015 


.486 




.975 


-.003 


.502 


.713 




.483 


.062 


.553 


.661 




.405 


.077 


-.764 


.875 




.384 


-.084 




Education 


' -.032 


.104 




.758 


-.030 


-.278 


158 


+ 


081 


164 


-.343 


.143 


* 


.018 


-.231 


.059 


.190 




.756 


.031 




Age 




.012 


.008 




.133 


.156 


-.010 


.012 




.393 


-.084 


-.003 


.011 




.797 


-,026 


-.028 


.015 


+ 


.067 


-.199 


Constant 


5.825 


.802 




.000 




7.838 


1.181 




.000 




8.267 


1.082 




.000 




5.600 


1.448 




.000 




* p<.05 




R-Squared-=.106;SEE 1.169 


R-Squared-.222;SEE 1.706 


R-Squared= .148; SEE 1.583 


R-Squared - .090; SEE 2.092 


+ p<JO 













































37 



Auto Theft 

Seriousness as the Dependent Variable 

The first regression focused on auto theft using "seriousness" as the dependent 
variable. An OLS regression addressing this relationship indicates several significant 
findings. First, the research hypothesis was unsupported as male respondents were not 
significantly more likely to view an auto theft as more serious than female 
respondents (b=.206, p=.418). However, respondents did view gender of the offender 
as a significant predictor of the seriousness of the auto theft (b=.500, p=.054). Male 
offenders were seen as being more serious than female offenders. The model is 
characterized by a decent fit as 12.6 percent of the variation in seriousness is 
explained for by the variables. 

Remorse as the Dependent Variable 

The next regression addressing auto theft used "remorse" as the dependent 
variable. An OLS regression addressing this dependent variable indicated that 
respondent's gender is not a significant predictor of whether individuals viewed the 
auto thieves as remorseful (b= -.133, p=.661). Also, the offender's gender was not a 
significant predictor of whether the public viewed a auto thief as remorseful. 
Specifically, male offenders were thought to be no more remorseful than female 



38 



offenders (b= -.415, p=.179). The model is characterized by a decent fit as 14.8 
percent of the variation in remorsefulness is explained for by the variables. 

Greed as the Dependent Variable 

The third regression addressing auto theft used "greed" as the dependent 
variable. An OLS regression addressing the dependent variable of greed indicates that 
respondent's gender is not a significant predictor of how individuals viewed greed of 
the auto thieves (b= -.072, p=.858). Offender's gender was not a significant predictor 
of whether the public viewed an auto thief as greedy. Specifically, male auto thieves 
were thought to be no more greedy than female auto theives (b=.387, p=.342). The 
model is characterized by a poor fit as 7.2 percent of the variation in greediness is 
explained for by the variables. 

Stress as the Dependent Variable 

The final regression addressing auto theft used "stress" as the dependent 
variable. An OLS regression addressing this dependent variable indicates that 
respondent's gender is not a significant predictor of how individuals viewed the 
criminals stress level (b= -.173, p=.673). In addition, the offender's gender was not a 
significant predictor of whether the public viewed the auto thief s actions as stress 
related. Male offenders were thought to be no more stress induced than female 



39 



offenders (b- -.415, p-.320). The model is characterized by a poor fit as 10.9 percent 
of the variation in stress is explained for by the variables. 



40 



Table 6: Regression analyses for Auto-Theft crimes 











Ordinary Least Squares (OLS) 










Seriousness 


Remorse 


Greed 


Stress 


V^ariables 


b 


SE 




p- 
value 


Beta 


b 


SE 




D- 

value 


Beta 


b 


SE 




D- 

value 


Beta 


b 


SE 




n~ 
value 


Beta 


Independent Variable 












































Gender of Respondent 














































Male 


.206 : 


.253 




.418 


.075 


-.133 


.303 




.661 


-.041 


-.072 


.401 




.858 


-.017 


-.173 


.407 




.673 


-.040 


Offender* s Gender 












































Male offender 


.500 


.256 


-f 


.054 


.181 


-.415 


.307 




.179 


-.126 


.387 


.406 




.342 


.093 


-.415 


.416 




.320 


-.096 


Respondent Characteristic 












































Marital Status 














































Never married 


-.J J 1 






1Q 1 


-.UoV 


1 o< 

-. 1 OJ 


.44 1 




.o /o 


-.U4z 


-.685 


583 




.242 


Izj 


"7 O C 

.78:? 


^ AO 

.608 




T AA 

.199 


.134 






Widowed 


1 

-.J J i 


.OHO 




.0 /V 


-.Uj / 


'1 oo 
.4VV 


1 1 O'^ 

1 . 1 vz 




.0 /o 


.Oj / 


-.365 ^ 1..19 


.783 


A'^ 

-.026 


-.490 


1 .341 




.715 


-.033 






Divorce 


1 Qd 
. 1 


. Joo 




.0 1 o 


(Wl 
.U"4 / 


-.4 J J 


.4 JO 




. JZJ 


oo A 

-.UV4 


.816 


.606 




1 OA 


. 1 j3 


AT 

.036 


.616 




.9r»4 


AA^ 

.UOo 






Separated 


1 JO 


.Do J 


* 




~ .Lri L 


-.OVj 


Q 1 n 
.o 1 U 




.jyZ 


-.UoU 


.499 


1.069 




.04 1 


A '1 C 


.946 


1 A 

1 .(Jo / 




.360 


AO 1 




Ennployed 


.244 


.274 




.374 


.085 


-.040 


.324 




.902 


-.012 


-.229 


.426 




.593 


-.053 


-.421 


.437 




.337 


-.094 




Victim of White -Collar Crime 


.332 


.306 




.280 


.098 


-.939 


.362 


* 


.011 


-.237 


-.234 


,474 




,622 


-.047 


-.260 


.491 




.597 


-.049 




Victim of Street Crime 


-.197 


.288 




.496 


-.060 


.256 


.345 




.460 


.065 


-.128 


.456 




.779 


-.025 


.278 


.464 




.550 


.054 




Race 
















































Black 


.281 


-475 




.555 


.053 


.485 


.563 




.391 


.077 


.230 


.710 




.747 


.030 


-.768 


.725 




.292 


-.097 






Hispanic 


-.167 


.443 




.707 


-.034 


-.335 


.522 




.523 


-.058 


-.323 


.690 




.641 


-.044 


-.060 


.702 




.932 


-.008 






Asian 


: -.148 


.848 




.862 


-.016 


1.098 


1.003 




.275 


.098 


-1.005 


1.324 




,449 


-.071 


1.306 


1.346 




.334 


.089 






Other Race 


.429 


1.054 




.685 


.037 


-.007 


1.246 




.995 


-.001 


1.638 


1.646 




.322 


.094 


2.238 


1.673 




.183 


.125 




Education 


-.215 


.126 




.091 


-.167 


-.310 


.150 


* 


.041 


-.202 


.027 


.197 




.891 


.014 


-.159 


.201 




.433 


-.079 




Age 




.001 


.009 




.892 


.014 


.020 


.011 




.067 


.192 


-.017 


.014 




.229 


-.131 


-.009 


.015 




.561 


-,062 


Constant 


5.564 


.918 




.000 




3.934 


1.050 




.000 




6.304 


1.388 




.000 




6.801 


1.420 




.000 




* p<.05 




R-Squared-.126;SEE 1.365 




R-Squared ^ . 

1.612 


148: SEE 




R-Squared-.072;SEE 
2.131 




R-Squared = .I09;SEE 
2.166 




+ p<.10 


















: 







































































41 



Burglary 

Seriousness as the Dependent Variable 

The first regression focused on burglary used "seriousness" as the dependent 
variable. An OLS regression addressing this relationship indicates several significant 
findings. First, the research hypothesis was not supported as male respondents were 
not significantly more likely to view a burglary as more serious than female 
respondents (b= -.123, p=.575). Respondents did not view gender of the offender as a 
significant predictor of the seriousness of burglary (b=.004, p=.985). The model is 
characterized by a decent fit as 13.8 percent of the variation in seriousness is 
explained for by the variables. 

Remorse as the Dependent Variable 

The next regression addressing burglary used "remorse" as the dependent 
variable. An OLS regression addressing this dependent variable indicates that 
respondent's gender is not a significant predictor of whether individuals viewed 
remorse of criminal (b=.384, p=.210). In contrast, the offender's gender was a 
significant predictor of whether the public viewed a burglar as remorseful. 
Specifically, male burglars were thought to be more remorseful than female burglarrs 
(b=.548, p=.066). The model is characterized by a good fit as 20.4 percent of the 
variation in remorsefulness is explained for by the variables. 



42 



Greed as the Dependent Variable 

The next regression addressing burglary used "greed" as the dependent 
variable. An OLS regression addressing this dependent variable indicates that 
respondent's gender is not a significant predictor of whether individuals viewed greed 
of criminal (b= -.013, p=.971). Also, the offender's gender was not a significant 
predictor of whether the public viewed a burglar as greedy. Male offenders were not 
thought to be more greedy than female offenders (b= -.030, p=.932). The model is 
characterized by a mediocre fit as 13.5 percent of the variation in greediness is 
explained for by the variables. 

Stress as the Dependent Variable 

The last regression addressing burglary used "stress" as the dependent 
variable. An OLS regression addressing this dependent variable indicates that 
respondent's gender is not a significant predictor of how individuals viewed the stress 
of the criminal (b=.562, p=.135). In addition, the offender's gender was not a 
significant predictor of how the public viewed the burglar's actions as stress related. 
Male burglars were thought to be no more stress induced than female burglars 
(b=.334, p=.358). The model is characterized by a decent fit as 15.8 percent of the 
variation in stress is explained for by the variables. 



43 



Table 7: Regression analyses for Burglary crimes 



Ordinary Least Squares (OLS) 









Seriousness 


Remorse 


Greed 


Stress 


Variables 


u 






P- 
value 


tseia 


D 






P- 
value 


Beta 


D 






P- 
value 


Beta 


L 

D 






P- 
value 


Beta 


Independent Variable 












































Gender of Respondent 














































Male 


-.123 


.219 




.575 


-.050 


.384 


.305 




.210 


.108 


-.013 


.358 




.971 


-.003 


.562 


.374 




.135 


.133 


Offender's Gender 












































Male offender 


.004 


.213 




.985 


.002 


.548 


.296 




.066 


.154 


-.030 


.346 




.932 




.334 


.361 




J58 


.079 


Respondent Characteristic 












































Marital Status 














































Never married 


-1.019 


.336 


* 


.003 


-.293 


.428 


.460 




.354 


.085 


-1.303 


.538 




.017 


-.233 


.519 


.562 




.357 


.088 






Widowed 


693 


,897 




.442 


.081 


.466 


1.243 




.708 


.038 


-.410 


1.453 




.778 


-.030 


2.127 


1.518 




.164 


.146 






Divorce 


1 v.^ 


.286 




,572 


-,049 


.435 


.403 




.282 


.090 


.186 


,480 




,700 


.034 


.288 


.501 




.567 


.050 






Separated 


-.658 


1.240 




,596 


-.045 


.977 


1.718 




.570 


.046 


-.310 


2.008 




,878 


-.013 


2.275 


2.098 




.280 


.091 




Employed 


.003 


.255 




.990 


.001 


-.268 


. _? J T- 




4S0 


. u U ^' 








07 S 


-. 1 DU 


oso 






QOO 
. vw / 


1 
. W I u 




Victim of White-Collar Crime 


.000 


.000 




098 


.145 


.000 


000 




J o o 


07 1 


000 


000 
.WWW 




70Q 




000 
. wwu 


000 
.000 






01 7 

, W i / 




Victim of Street Crime 


.000 


.000 






1 16 

. 1 J u 


000 
. www 


, www 








000 


000 






-, 1 Oj 


000 
. WUw 


000 




07 1 
.o / i 


1 AO 




Race 












































Black 


-.526 


.432 




.226 


-.109 


.674 


,599 




,262 


.097 


-.345 


.701 




.624 


-.044 


.436 


.732 




.553 


.053 






Flispanic 


.017 


.415 




.968 


,003 


1.848 


.575 


* 


.002 


.266 


-.177 


.672 




,792 


-.023 


.671 


.702 




.341 


.082 






Asian 


-.043 


1.502 




.977 


-.003 


1.210 


2.082 




.562 


.057 


2.787 


2.433 




,254 


.118 


1.242 


2.542 




626 


.049 






Other Race 


-1.126 


.886 




.206 


-.107 


-.267 


1.227 




.828 


-.018 


-.856 


1.434 




.552 


-.051 


2.563 


1.498 




.090 


.144 




Education 


.123 


.106 




.251 


.106 






* 


01 


-.218 


-.176 


.171 




.307 


-.094 


.150 


.179 




.403 


.076 




Age \ 


022 


.009 


* 


,015 


-.243 


.007 


.013 




.566 


.055 


-.003 


.015 




.842 


-.020 


-.042 


.015 




.007 


-.269 


Constant 


7.421 


.826 




.000 




2.721 


1.145 




.019 




7.698 


1.338 




.000 




4.496 


1.398 




.002 




* p<.05 




R-Squared-.138;SEE 1.219 


R-Squared = .204; SEE 1.689 


R-Squared- .135; SEE 
1.973 




R-Squared- .158; SEE 
2.062 




+ p<.10 













































44 



Corporate Crime 

Seriousness as the Dependent Variable 

The first regression focused on corporate crimes used "seriousness" as the 
dependent variable. An OLS regression addressing this relationship indicates several 
significant findings. First, the research hypothesis was not supported as male 
respondents were not significantly more likely to view a corporate crime as more 
serious than female respondents (b=.218, p=.458). Respondents did not view gender 
of the offender as a significant predictor of the seriousness of corporate crime 
(b=.084, p=.762). The model is characterized by a poor fit as only 10.1 percent of the 
variation in seriousness is explained for by the variables. 

Remorse as the Dependent Variable 

The second regression addressing corporate crime used "remorse" as the 
dependent variable. An OLS regression addressing this dependent variable indicates 
that respondent's gender is not a significant predictor of how individuals viewed the 
corporate criminals remorse (b= -.121, p-.709). In contrast, the offender's gender was 
a significant predictor of whether the public viewed a corporate criminal as 
remorseful. Specifically, male offenders were thought to be less remorseful than 
female offenders (b= -.538, p=.087). The model is characterized by a good fit as 21 
percent of the variation in remorsefulness is explained for by the variables. 



45 



Greed as the Dependent Variable 

The third regression addressing corporate crime used "greed" as the dependent 
variable. An OLS regression addressing this dependent variable indicates that 
respondent's gender is not a significant predictor of how individuals viewed the greed 
of corporate criminals (b= -.248, p=.405). Also, the offender's gender was not a 
significant predictor of whether the public viewed a corporate criminal as greedy. 
Specifically, male offenders were thought to be no more greedy than female offenders 
(b=.447, p=.l 19). The model is characterized by a decent fit as 1 1.9 percent of the 
variation in greediness is explained for by the variables. 

Stress as the Dependent Variable 

The last regression addressing corporate crime used "stress" as the dependent 
variable. An OLS regression addressing this dependent variable indicates that 
respondent's gender is not a significant predictor of how individuals viewed the stress 
of the criminal (b=.021, p=.358). In addition, the offender's gender was not a 
significant predictor of whether the public viewed the corporate criminal's actions as 
stress related. Male offenders were not thought to be more stress induced than female 
offenders (b=.264, p=.488). The model is characterized by a moderately decent fit as 
17.8 percent of the variation in stress is explained for by the variables. 



46 



Table 8: Regression analyses for corporate crimes 











Ordinary Least Squares (OLS) 










Seriousness 


Remorse 


Greed 


Stress 


Variables 


b 


SE 




P- 

value 


Beta 


b 


SE 




p- 

value 


Beta 


K 

u 


OIL 




P- 
value 


DL la 


h 






P- 
value 


Ocla 


Independent Variable 












































Gender of Respondent 














































Male 


T 1 






/ICO 

.458 


AT) 

.073 


-.121 


.324 




.709 


-.034 


-.248 


.297 




.405 


-.081 


.021 


.395 




.958 


.005 


Offender's Gender 












































Male offender 


.084 


.278 




.762 


.028 


-.538 


.311 




.087 


1"" 


.447 


.285 




.119 


.146 


.264 


.380 




.488 


.063 


Respondent Characteristic 












































Marital Status 














































Never married 


A A 1 

-.44 1 


A C A 

.4j4 




o 1 o 
.333 


-. 1 13 


.520 


.503 




.304 


.113 


-.355 


.460 




.442 


-.089 


-.379 


.612 




.537 


-.070 






Widowed 


1 .475 


1.553 




.344 


.085 


-2.000 


1.720 




.247 


-.098 


1.133 


1.575 




.473 


.064 


-3.663 


2.090 




.082 


-. 1 52 






Divorce 


.299 


.396 




.451 


.075 


.077 


.445 




.862 


.016 


.018 


.408 




.965 


.004 


-1.154 


.551 


* 


.038 


-.202 






Separated 


-1 .755 


.825 




.035 


-.200 


-.528 


.915 




.565 


-.051 


-2.235 


.837 




.009 


-.249 


-3.307 


1.1 12 




.004 


-.271 




Employed 


-.365 


.337 




.281 


-,1 10 


.325 


.376 




.388 


.083 


-.513 


.344 




.139 


-.152 


-.313 


.458 




.496 


-.068 




Victim of White-Collar Crime 


.000 


.000 




.619 


.064 


-.001 


.000 


* 


.006 


-.341 


.000 


.000 




.967 


-.005 


.000 


.000 




.376 


-.1 10 




Victim of Street Crime 


.011 


.335 




.975 


.003 


.214 


.372 




.566 


.051 


-.043 


.340 




.900 


-.012 


-.184 


.452 




.685 


-.037 




Race 














































Black 


.137 


.797 




.864 


.017 


-.396 


.883 




.654 


-.043 


.580 


.808 




.475 


.072 


.464 


1.073 




.666 


.042 






Hispanic 


-.799 


.442 




.074 


-.173 


1.119 


.490 


* 


.024 


.206 


-.027 


.449 




953 


-.006 


.924 


596 




,124 


.144 






Asian 


-.107 


1.099 




.923 


-.009 


3.794 


1.218 


* 


.002 


.261 


.311 


1.115 




.781 


.025 


2.276 


1.480 




.127 


133 






Other Race 


.655 


1.652 




.692 


.053 


4.717 


1.830 


* 


.011 


.325 


1.324 


1.675 




,431 


.105 


3.128 


2.227 




.163 


.182 




Education 


-.155 


.137 




.260 


-.117 


.085 


.152 




.575 


.055 


-.129 


.139 




.354 


-.096 


-.165 


.189 




.382 


-.088 




Age 




.001 


.012 




.964 


.006 


.017 


.014 




.207 


.145 


-.013 


.012 




.308 


-.124 


-.009 


.017 




.586 


-.064 


Constant 


7.300 


1.057 




.000 




2.234 


1.171 




.059 




8.415 


1.072 




.000 




5.790 


1.438 




.000 




* p<.05 




R-Squared- .101; SEE 1.504 


R-Squared-.210; SEE 1.667 


R-Squared-.119; SEE 1.526 




R-Squared- .178; SEE 2.025 




+ p<.10 













































47 



Prostitution 

Seriousness as the Dependent Variable 

The first regression focused on prostitution used "seriousness" as the 
dependent variable. An OLS regression addressing this relationship indicates several 
significant findings. First, the research hypothesis was supported as male respondents 
were significantly more likely to view prostitution as less serious than were female 
respondents (b= -.654, p=.057). Respondents did not view gender of the offender as a 
significant predictor of the seriousness of prostitution (b=.201, p==.546). The model is 
characterized by a decent fit as 12.8 percent of the variation in seriousness is 
explained for by the variables. 

Remorse as the Dependent Variable 

The next regression addressing prostitution used "remorse" as the dependent 
variable. An OLS regression addressing this dependent variable indicates that 
respondent's gender is not a significant predictor of whether individuals viewed 
remorse of the prostitutes (b= -.435, p=.176). In addition, the offender's gender was 
not a significant predictor of whether the public viewed a prostitute as remorseful. 
Specifically, male prostitutes were thought to be no more remorseful than female 
prostitutes (b=-.415, p=.181). The model is characterized by an average fit as 13.5 
percent of the variation in remorsefulness is explained for by the variables. 



48 



Greed as the Dependent Variable 

The next regression addressing prostitution used "greed" as the dependent 
variable. An OLS regression addressing this dependent variable indicates that 
respondent's gender is not a significant predictor or whether individuals viewed greed 
of criminal (b=.236, p=.495). Similarly, the offender's gender was also not a 
significant predictor of whether the public viewed a prostitutes actions as greedy. 
Specifically, male offenders were not thought to be more greedy than female 
offenders (b= -.009, p=.979). The model is characterized by a good fit as 21 .8 percent 
of the variation in greediness is explained for by the variables. 

Stress as the Dependent Variable 

The last regression addressing prostitution used "stress" as the dependent 
variable. An OLS regression addressing this dependent variable indicates that 
respondent's gender is not a significant predictor of how individuals viewed stress of 
criminal (b= -.144, p=.714). Addhionally, the offender's gender was not a significant 
predictor of whether the public viewed the prostitutes actions as stress related. Male 
prostitutes were thought to be no more stress induced than female prostitutes (b= - 
.184, p=.629). The model is characterized by a poor fit as 9.7 percent of the variation 
in stress is explained for by the variables. 



49 



Table 9: Regression analyses for prostitution crimes 



Ordinary Least Squares (OLS) 



Seriousness 



Remorse 



Variables 


u 






P- 
value 


oeta 


D 


or. 




P- 
value 


t>eta 


D 






P- 
value 


Beta 


D 






P- 
vaiue 


Beta 


Independent Variable 












































Gender of Respondent 














































Male 




.J'-T 1 




• WJ / 






1 Q 

.jiy 




.1/0 


1 90 
-. I zu 








Ay J 


.Uj / 


1 A A 
-. 1 44 


. jV 1 




7 1 A 
. / 14 


-.UJ J 


Offender's Gender 












































Male offender 


.201 ' 


.332 




.546 


.052 


-.415 


.308 




.181 


-.117 


-.009 


.335 




.979 


-.002 


-.184 


.380 




.629 


-.043 


Respondent Characteristic 










































— 


Marital Status 














































Never married 




. jyjo 




1.QA 


-.Uo / 


/I /I "> 






1/17 


.UVo 




. jUo 




C\(\A 


7 7A 

-.1 /o 




C7 A 

.D lb 


+ 


.(Joo 


1 7A 
. 1 /O 






Widowed 


-1. 145 


.933 




.222 


-.109 


.437 


.859 




.612 


.046 


.228 


.937 




.808 


.021 


.672 


1.061 




.527 


.058 






Divorce 


1.035 


.496 




,039 


.178 


.059 


.469 




.901 


.01 1 


.816 


.498 




.104 


.134 


1.068 


.565 


+ 


.061 


.166 






Separated 


.039 


1.168 




.974 


.003 


.492 


1.346 




.715 


.033 


1.893 


1.177 




.110 


.134 


1.062 


1.334 




.428 


,071 




Employed 


.119 


.400 




.767 


.026 


.323 


.377 




.393 


.076 


.395 


.402 




,328 


.081 


-.088 


.458 




.848 


-.017 




Victim of White-Collar Crime 


.000 


.000 




.616 


.061 


.000 


.000 




.281 


-.095 


.000 


.000 




.765 


-.025 


.000 


.000 




.889 


-.012 




Victim of Street Crime 


.000 


.000 




.708 


.044 


-.158 


.327 




.628 


-.042 


-.421 


.361 




.245 


-.095 


.209 


.411 




.612 


.045 




Race 














































Black 


-1.021 


.811 




.210 






/47 




632 


-.041 


-1.168 


.813 




.154 


1 1 A 
-.110 


-.928 


1.022 




.366 


-.WoU 






Hispanic 


.595 


.834 




.477 


.062 


.065 


.779 




.934 


.007 


-1.183 


.836 




.160 


-.117 


-.494 


.948 




.603 


-.046 






Asian 


.725 


1.142 




.527 


.054 


-L888 


1 049 


+ 


.074 


-.154 


730 


1.145 




.525 
.278'^ 


052 


-.821 


1.298 




528 


-.055 






Other Race 


-.613 


.764 




.424 


-.069 


-l.Olu 






.156 


-.124 


=.842 


nil 




-.090 


-L203 


.874 




171 


.122 




Education 


094 


.167 




.574 


.048 


.312 


.154 


* 


.044 


.176 


-.521 


.166 


* 


.002 


-.256 


.103 


.188 




.585 


.048 




Age 




-.010 


.014 




.469 


-.076 


-.020 


.013 




.119 


-.165 


-.035 


.014 


* 


.014 


-.247 


-.022 


.016 




.174 


-.146 


Constant 


5.293 


1.319 




.000 




3.772 


1.223 




.003 




6,161 


1.308 




.000 




6.015 


1.485 




.000 




* p<.05 




R-Squared = .128;SEE 1.90 




R-Squared = 


.135; SEE 1.746 


R-Squared- .218; SEE 
1.904 




R-Squared = .( 
2.158 


)97; SEE 




+ p<.10 













































Greed 



Stress 



50 



Binary Logistic Regression Analysis 

This section focuses on the dependent variable of "punishment". UnHke the 
dependent variables used earlier in this thesis, this dependent variable is measured as 
a dichotomy. For this reason, binary logistic regressions are utilized to address the 
research questions. In addition, unlike the previous sections, these analyses focus on 
the aggregation of white collar crimes (i.e., Ponzi schemes, embezzlement, and 
corporate crime), and street crimes (i.e., burglary, auto theft and prostitution). 

White Collar Crime 

The first logistic regression model focuses on the influence of respondent's 
gender on the severity of the punishment given to the offender in the scenarios. 
Findings in Table 9 indicate that respondent's gender is a significant predictor of the 
severity of the punishment given to the offender (b= -.505, p= .090). In contrast, the 
offender's gender is not a significant predictor of the dependent variables (b=440, p= 
.143). In particular, female offenders were not considered to have received less severe 
punishment than their male counterparts. 

Street Crime 

The second logistic regression model focuses on the influence of respondent's 
gender on the severity of the punishment given to the offender in the various street 
crime scenarios. Table 9 shows the findings from this regression that indicate that the 



51 



respondent's gender was not a significant predictor of the severity of punishment 
given to the offender (b=.359, p=.153). In addition, findings revealed that the 
offender's gender is not a significant predictor of the dependent variable (b=.226, 
p=.370). 



Table 10: Binary Logistic Regression analyses for White Collar and Street Crimes 





1 I 
1 \ 


White Collar Crimes 


Street Crimes 


Variables 


b 


SE 




P 

value 


AOR 


b 


SE 




P- 
value 


AOR 






























Independent Variable 
























Gender of Respondent 


























Male 


-.505 


.298 


+ 


.090 


.603 


.359 


.252 




.153 


1.433 






























Offender's Gender 
























Male offender 


.440 


.301 




.143 


1.553 


.226 


.252 




.370 


1.254 






























Respondent 
Characteristic 
























Marital Status 


























Never married 


-.303 


.423 




.473 


.738 


-.647 


.350 


+ 


.065 


.523 






Widowed 


19.486 


20034 




.999 


290200322 


-.810 


.700 




.247 


.445 






Divorce 


.1 13 


.486 




.817 


1.119 


.511 


.390 




.190 


1.667 






Separated 


-2.033 


.988 


* 


.040 


.131 


-.234 


.756 




.757 


.792 




Employed 












-.376 


.286 




.189 


.686 




Victim of White- 
Collar Crime 


.130 


.452 




.773 


1.139 


-.477 


.328 




.146 


.621 




Victim of Street Crime 


-.509 


.379 




.179 


.601 


.405 


.272 




.137 


1.500 




Race 


























Black 


-.863 


.642 




.179 


.422 


-.612 


.482 




.205 


.542 






Hispanic 


-.137 


.527 




.795 


.872 


1.265 


.651 


* 


.052 


3.542 






Asian 


19.999 


17602 




.999 


484896420 ' 


-.153 


.818 




.852 


.858 






Other Race 


-1.51 1 


.838 


+ 


.071 


.221 


-.834 


.956 




.383 


.434 




Ed 


ucation 


-.186 


.139 




.181 


.830 


.360 


.125 


* 


.004 


1.434 




Age 




-.002 


.012 




.888 


.998 


-.013 


.010 




.182 


.987 


Cc 


)nstant 




3.103 


1.543 




.044 


22.263 


.198 


1.179 




.866 


1.220 


* p<.05 
























+ p<.10 

























52 



CHAPTER 6 
DISCUSSION AND CONCLUSIONS 

The findings in this research disagree with previous studies that discovered 

street crime is viewed by the pubHc as more serious than white-collar crime. In the 

current study, Ponzi and embezzlement schemes were seen as more serious than any 

of the street crimes including burglary, auto theft, and prostitution. Overall, 

prostitution was seen as the least serious offense. The data suggest that public 

perceptions on the seriousness of white-collar crime have changed, perhaps because 

of increased media attention. The media coverage also may have resulted in more 

public exposure about the costs and consequences of white-collar crime. The high 

number of substantial financial damages incurred as a result of Ponzi schemes in the 

last five years certainly may have contributed to these changed viewpoints. 

Perceptions of how male and female respondents viewed street versus white- 
collar crimes varied between genders and type of crime. Males are more likely to 
believe that street level crimes are more deserving of punishment compared to 
females. In contrast, men were more likely than women to believe that white-collar 
offenders should receive less severe punishment. These differences may be 
attributable to the characteristics of the sample; that is, working-class males may see 
their own schemas and actions in the workplace as more ''similar" to white-collar 
offenders. Lack of findings within this current study may be attributed to the lack of 



53 



variability within the variables, which is the reason for lack of relationships within the 
findings sections. 

Future research should attempt to account for this public shift in perceptions 
of white-collar crime and may include surveys and interviews that can account for the 
shift in opinion. It may be important, for example, to ask respondents where they get 
their information about white-collar crime and how often they hear about such 
offenses in the media. Asking these types of questions may help more fully account 
for the change of the public's perception of seriousness of white-collar crime. 

Future research may also consider asking the religious orientation of survey 
respondents. Data suggest that individuals who identified as Hispanic viewed 
offenders as more remorseful. Asking religious orientation of respondents might aid 
in understanding if this variable is influencing how individuals view the remorse of 
criminal offenders. Different religious cultures may sway individuals' views on 
seriousness, remorse, greed, stress, and punishment. 

Another future research recommendation is asking the political affiliation and 
political ideology of survey respondents. An individual's political affiliation may 
heavily influence the way individuals perceive the various crime scenarios. 
Republicans may see crime as deserving of different punishment than Democrats or 
Independent affiliated individuals. In addition, polifical ideology would let the 
researcher understand how individuals that have different ideological thought process 



54 



perceive various crimes. If an individual identifies themselves as conservative, they 
may be more likely to punish and perceive crime differently than an individual who 
identifies themselves as a liberal thinker. 

Lastly, due to the geographic location in which this research took place, 
asking survey respondents to identify whether or not they are currently serving in the 
military, or have served in the military in the past. Since El Paso County has such a 
military density, identifying past and present military background would help 
establish if there is a relationship between crime perception and military background. 
In addition, research could establish if military background is linked to other 
important independent variables that establish relationships in how individuals 
perceive crime, and crime seriousness. 

There are several caveats associated with the current research. First, though 
the sample was a random sample from a jury pool, subjects may well have self- 
selected to participate based on any number of characteristics. The large sample size, 
however, assists in minimizing self-selection problems and extending generalizations. 
Second, the crime scenarios may have lacked adequate depth in the descriptions of 
the offenders' behaviors and outcomes. Third, participants may have been unclear of 
the nature of the exact offenses, particularly for the corporate crime. 

While the current research failed to find significance of views between male 
and female offenders on individuals perception of how they view the various crimes. 



55 



the results offered other insight into how individuals perceive crime. This research, 
unlike previous studies, suggests that the public views white-collar crime as serious. 
The most distinctive piece of information this research offers is insight into the 
changing awareness of white-collar crime and how the public's perception of the once 
greatly un-dealt with elite deviance is now changing. 



56 



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58